Down stream analysis of the dataset ‘4_A549_27plex’

setup

knitr::opts_chunk$set(dev = "png",dpi = 80)
library(ggforce)
library(tidyverse)
dcolor <- ggsci::scale_color_d3('category20')
dfill <- ggsci::scale_fill_d3('category20')
ccolor <- viridis::scale_color_viridis()
style <- function(x,color,method='UMAP',axis=c(1,2),Theme=NULL,
                  axis.text=FALSE,axis.title=FALSE,coord_fix =TRUE,
                  palette=TRUE,title=NULL,
                  legend=TRUE,scale_color_log10=FALSE,
                  guide_pointSize=2.5,...) {
  axis <- paste0(method,axis)
  if(is.null(Theme)) Theme <- theme_bw
  g <- ggplot() + Theme()
  g <- g + geom_point(aes_(x=as.name(axis[1]),y=as.name(axis[2]),color=as.name(color)),x,...)
  
  if(coord_fix) {
    g <- g + coord_fixed()
  }
  if(!axis.text) {
    g <- g + theme(axis.text = element_blank(),axis.ticks = element_blank())
  }
  if(!axis.title) {
    g <- g + theme(axis.title = element_blank())
  }
  if(is.null(title)) {
    g <- g + ggtitle(method) 
  }else if(!is.na(title)) {
    g <- g + ggtitle(title) 
  }
  if(!legend) g <- g + theme(legend.position = 'none')
  
  if(scale_type(as.matrix(x[,color]))=='discrete' & !is.null(guide_pointSize)) {
    g <- g + guides(color = guide_legend(override.aes = list(size=guide_pointSize)))
  }
  
  if(palette) {
    if(scale_type(as.matrix(x[,color]))=='discrete') {
      g <- g + ggsci::scale_color_d3('category20')
    }else{
      if(scale_color_log10){
        g <- g + viridis::scale_color_viridis(trans='log10')
      }else{
        g <- g + viridis::scale_color_viridis()
      }
    }
  }
  return(g)
}
tib2df <- function(tib,RowNames=1) {
  rn <- pull(tib,all_of(RowNames))
  df <- tib %>%
    select(-RowNames) %>%
    as.data.frame()
  rownames(df) <- rn
  return(df)
}

load data

data <- tibble(
  path = list.files("4_A549_27plex/Raw/",full.names = T),
  sampleName = c('0h-r1', '0h-r2', '0p5h-r1', '0p5h-r2', '24h-r1', '24h-r2', '48h-r1', '48h-r2')
  ) %>%
  separate(sampleName,into=c('treat','rep'),remove = F) %>%
  mutate(
    metric = map(path,~{
      .x %>%
        read_csv(show_col_types=F) %>% 
        unite(cell,tileID,cellID,sep = '-',remove = F)
      }),
    IF = map(metric,~{.x[,-(2:8)]}),
    meta = map2(metric,IF,~{
      .x[,1:8] %>%
        unite(cell,tileID,cellID,sep = '-',remove = F) %>%
        mutate(totalExp = rowSums(.y[,-1]))
      }),
    ) %>%
  select(-path,-metric)

Quality check & control

data %>%
  select(-IF) %>% unnest(meta) %>%
  ggplot(aes(x,y,color=treat)) +
    theme_void() +
    geom_point(size=.1) + dcolor +
    facet_grid(rep~treat) + coord_fixed()

data %>%
  select(sampleName,meta) %>% unnest(meta) %>%
  ggplot(aes(sampleName,totalExp)) + geom_violin() + geom_boxplot()

dataf <- data %>%
  mutate(
    idx = map(meta,~{.x$totalExp > 1.3}),
    IF = map2(IF,idx,~{.x[.y,]}),
    meta = map2(meta,idx,~{.x[.y,]})
    ) %>%
  mutate(
    idx2 = map(IF,~{rowSums(.x[,-1]==0) == 0}),
    IF = map2(IF,idx2,~{.x[.y,]}),
    meta = map2(meta,idx2,~{.x[.y,]}),
    nCell = map(idx2,sum) %>% unlist()
    ) %>%
  select(-idx,-idx2)

dataf %>%
  select(sampleName,meta) %>% unnest(meta) %>%
  ggplot(aes(sampleName,totalExp)) + 
  geom_violin() + geom_boxplot()

dataf %>%  
  ggplot(aes(sampleName,nCell)) + 
  theme_classic() +
  geom_bar(stat='identity') +
  scale_x_discrete(limits=rev) +
  theme(axis.title.y = element_blank()) +  
  coord_flip() + theme(aspect.ratio = .35) 

tmp <- dataf %>% select(sampleName,IF) %>% unnest(IF)
idx <- with(tmp,model.matrix(~0+sampleName))
colnames(idx) <- sub("sampleName","",colnames(idx))
avgmat <- tmp[,-(1:2)] %>% as.matrix() %>% t %>% {t(.%*%idx)/colSums(idx)} %>% scale %>% asinh %>% t 
pheatmap::pheatmap(avgmat,cluster_cols = F,cellwidth = 10,color=viridis::viridis(10),show_rownames = T)

Meta <- dataf %>%
  select(sampleName,treat,rep,meta) %>% unnest(meta) %>%
  unite(Cell,treat,rep,cell,sep = '-',remove = F)
X <- bind_rows(dataf$IF) %>% select(-cell) %>% 
   scale() %>% asinh()
rownames(X) <- Meta$Cell
p <- prcomp(X)
screeplot(p,type='l',n=30)

total <- bind_rows(dataf$meta) %>% pull(totalExp) 
cor(p$x,total) %>%
  plot(xlab = 'PC',ylab = 'CorCoef_vsTotalExp')

um <- uwot::umap(p$x[,2:6],
                 metric = 'cosine',
                 n_neighbors = 30,
                 min_dist = .1) %>%
  as_tibble(rownames = 'Cell') %>%
  dplyr::rename(UMAP1=2,UMAP2=3)
Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if `.name_repair` is omitted as of tibble 2.0.0.
Using compatibility `.name_repair`.
Metaum <- inner_join(Meta,um,by = 'Cell')

style(Metaum,'treat',title = NULL,size = .1,alpha = .6)
Warning: `aes_()` was deprecated in ggplot2 3.0.0.
Please use tidy evaluation idioms with `aes()`

style(Metaum,'sampleName',title = NA,size = .1,alpha = .6)

style(Metaum,'totalExp',title = NA,size = .1,alpha = .6)

Pseudo time

Using the datasets of 0h and 0.5h treatment

Meta2 <- Meta %>% filter(treat %in% c('0h','0p5h'))
X2 <- dataf %>%
  filter(treat %in% c('0h','0p5h')) %>%
  pull(IF) %>% bind_rows() %>% 
  select(-cell) %>% 
  scale() %>% asinh()
rownames(X2) <- Meta2$Cell
p2 <- prcomp(X2)
screeplot(p2,type='l',n=30)

total2 <- Meta2$totalExp
cor(p2$x,total2) %>%
  plot(xlab = 'PC',ylab = 'CorCoef_vsTotalExp')

um2 <- uwot::umap(p2$x[,2:6],
                 metric = 'cosine',
                 n_neighbors = 30,
                 min_dist = .1) %>%
  as_tibble(rownames = 'Cell') %>%
  dplyr::rename(UMAP1=2,UMAP2=3)
Metaum2 <- inner_join(Meta2,um2,by = 'Cell')
style(Metaum2,'treat',title = NULL,size = .1,alpha = .6)

ph2 <- phateR::phate(p2$x[,2:6],
                     mds.dist.method = 'cosine',
                     knn = 20)
Calculating PHATE...
  Running PHATE on 19597 observations and 5 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 2.02 seconds.
    Calculating affinities...
    Calculated affinities in 0.10 seconds.
  Calculated graph and diffusion operator in 2.14 seconds.
  Calculating landmark operator...
    Calculating SVD...
    Calculated SVD in 1.36 seconds.
    Calculating KMeans...
    Calculated KMeans in 2.33 seconds.
  Calculated landmark operator in 4.20 seconds.
  Calculating optimal t...
    Automatically selected t = 26
  Calculated optimal t in 2.87 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.58 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 5.23 seconds.
Calculated PHATE in 15.02 seconds.
Metaum2 <- Metaum2 %>%
  mutate(PHATE1 = ph2$embedding[,1],PHATE2 = ph2$embedding[,2])
style(Metaum2,'treat','PHATE',size=.1)

style(Metaum2,'rep','PHATE',size=.1,palette = F)

set.seed(1); km <- kmeans(ph2$embedding,centers=5)
Metaum2 %>% 
  mutate(km=as.factor(km$cluster)) %>%
  style('km','PHATE',size=.1)

library(slingshot)
Loading required package: princurve
Loading required package: TrajectoryUtils
Loading required package: SingleCellExperiment
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

Attaching package: ‘matrixStats’

The following object is masked _by_ ‘.GlobalEnv’:

    count

The following object is masked from ‘package:dplyr’:

    count


Attaching package: ‘MatrixGenerics’

The following objects are masked from ‘package:matrixStats’:

    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins, colCumprods, colCumsums, colDiffs, colIQRDiffs, colIQRs, colLogSumExps,
    colMadDiffs, colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, colSums2, colTabulates,
    colVarDiffs, colVars, colWeightedMads, colWeightedMeans, colWeightedMedians, colWeightedSds, colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, rowCollapse,
    rowCounts, rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, rowWeightedMads, rowWeightedMeans,
    rowWeightedMedians, rowWeightedSds, rowWeightedVars

Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: ‘Biobase’

The following object is masked from ‘package:MatrixGenerics’:

    rowMedians

The following objects are masked from ‘package:matrixStats’:

    anyMissing, rowMedians


Attaching package: ‘SummarizedExperiment’

The following object is masked from ‘package:Seurat’:

    Assays

The following object is masked from ‘package:SeuratObject’:

    Assays
sce <- getLineages(ph2$embedding,as.character(km$cluster),start.clus = '5') %>%
  getCurves() %>% slingPseudotime() %>%
  as_tibble(rownames='Cell') %>% mutate(Lineage1 = Lineage1/max(Lineage1)) %>%
  rename(PseudoTime = Lineage1)
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = TRUE.Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = TRUE.Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = TRUE.Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = TRUE.Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = TRUE.
Metaum2 <- left_join(Metaum2,sce,by='Cell')
Metaum2 %>% style("PseudoTime","PHATE",size=.2) + theme_void()

Metaum2 %>% style("PseudoTime","UMAP",size=.2) + theme_void()

Metaum2 %>%
  ggplot(aes(PseudoTime,fill=treat)) + geom_histogram(position = 'dodge') + theme_classic() + dfill + coord_fixed(ratio=.0003) +
  theme(axis.ticks = element_blank(),panel.grid = element_blank())

LOESS <- dataf %>%
  filter(treat %in% c("0h","0p5h")) %>%
  select(sampleName,IF) %>% unnest(IF) %>%
  unite(Cell,sampleName,cell,sep='-') %>%
  left_join(select(Metaum2,Cell,PseudoTime),.) %>%
  gather(Prot,ExpLevel,-(1:2)) %>%
  group_by(Prot) %>% nest() %>%
  mutate(LOESS = map(data,~loess(ExpLevel~PseudoTime,.x,span = 0.2)),
         PRED = map(LOESS,~predict(.x,newdata=seq(0,1,by=0.01))),
         newdata = map(PRED,~tibble(PseudoTime=seq(0,1,by=0.01),PRED=.x)) )
Joining with `by = join_by(Cell)`
loessPl <- function(x,ratio = .35){
  ggplot(x,aes(PseudoTime,scaled,color=Prot)) + 
  geom_line() + theme_bw() +
  theme(legend.position = 'none') +
  theme(axis.ticks.y = element_blank(),
        strip.background = element_rect(fill=NA,color=NA), 
        panel.grid.major.x = element_blank(),panel.grid.minor = element_blank()) + 
  coord_fixed(ratio=ratio) + labs(y = 'scaled expression level')
}

LOESS_scaled <- LOESS %>% select(Prot,newdata) %>% 
  mutate(newdata = map(newdata,~mutate(.x,scaled = (PRED-min(PRED))/(max(PRED)-min(PRED)) ))) %>%
  unnest(newdata)  %>% ungroup()

usep <- c("p4EBP1/2/3","pATM","pELF2A","pERK","pFAK","pp38","pSMAD1/5","pSMAD3","pSTAT3")
LOESS_scaled %>% filter(Prot %in% usep) %>% loessPl()

LOESS_scaled %>% filter(Prot %in% usep) %>% loessPl() + facet_wrap(~Prot)

LOESS_scaled %>% filter(!Prot %in% usep) %>% loessPl() + facet_wrap(~Prot)

LOESS_scaled %>% filter(!Prot %in% usep) %>% loessPl()

sessionInfo()
R version 4.2.2 (2022-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.2.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] slingshot_2.6.0             TrajectoryUtils_1.6.0       SingleCellExperiment_1.20.1 SummarizedExperiment_1.28.0 Biobase_2.58.0              MatrixGenerics_1.10.0       matrixStats_1.0.0          
 [8] princurve_2.1.6             lubridate_1.9.2             forcats_1.0.0               stringr_1.5.0               dplyr_1.1.2                 purrr_1.0.1                 readr_2.1.4                
[15] tidyr_1.3.0                 tibble_3.2.1                tidyverse_2.0.0             ggforce_0.4.1               ggplot2_3.4.2               Seurat_4.3.0.1              SeuratObject_4.1.3         
[22] sp_2.0-0                    GenomicRanges_1.50.2        GenomeInfoDb_1.34.9         IRanges_2.32.0              S4Vectors_0.36.2            BiocGenerics_0.44.0        

loaded via a namespace (and not attached):
  [1] utf8_1.2.3                spatstat.explore_3.2-1    reticulate_1.30           tidyselect_1.2.0          RSQLite_2.3.1             AnnotationDbi_1.60.2      htmlwidgets_1.6.2        
  [8] grid_4.2.2                Rtsne_0.16                munsell_0.5.0             codetools_0.2-19          ica_1.0-3                 future_1.33.0             miniUI_0.1.1.1           
 [15] withr_2.5.0               spatstat.random_3.1-5     colorspace_2.1-0          progressr_0.13.0          knitr_1.43                rstudioapi_0.15.0         ROCR_1.0-11              
 [22] tensor_1.5                listenv_0.9.0             labeling_0.4.2            GenomeInfoDbData_1.2.9    polyclip_1.10-4           bit64_4.0.5               farver_2.1.1             
 [29] pheatmap_1.0.12           rprojroot_2.0.3           parallelly_1.36.0         vctrs_0.6.3               generics_0.1.3            xfun_0.39                 timechange_0.2.0         
 [36] R6_2.5.1                  phateR_1.0.7              bitops_1.0-7              spatstat.utils_3.0-3      cachem_1.0.8              DelayedArray_0.24.0       promises_1.2.0.1         
 [43] scales_1.2.1              vroom_1.6.3               gtable_0.3.3              globals_0.16.2            goftest_1.2-3             rlang_1.1.1               pamr_1.56.1              
 [50] splines_4.2.2             lazyeval_0.2.2            spatstat.geom_3.2-4       yaml_2.3.7                reshape2_1.4.4            abind_1.4-5               stylo_0.7.4              
 [57] httpuv_1.6.11             tools_4.2.2               tcltk_4.2.2               ellipsis_0.3.2            jquerylib_0.1.4           RColorBrewer_1.1-3        ggdendro_0.1.23          
 [64] proxy_0.4-27              ggridges_0.5.4            Rcpp_1.0.11               plyr_1.8.8                sparseMatrixStats_1.10.0  zlibbioc_1.44.0           RCurl_1.98-1.12          
 [71] deldir_1.0-9              pbapply_1.7-2             viridis_0.6.4             cowplot_1.1.1             zoo_1.8-12                ggrepel_0.9.3             cluster_2.1.4            
 [78] here_1.0.1                magrittr_2.0.3            data.table_1.14.8         scattermore_1.2           lmtest_0.9-40             RANN_2.6.1                fitdistrplus_1.1-11      
 [85] hms_1.1.3                 patchwork_1.1.2           mime_0.12                 evaluate_0.21             xtable_1.8-4              gridExtra_2.3             compiler_4.2.2           
 [92] KernSmooth_2.23-22        crayon_1.5.2              htmltools_0.5.5           later_1.3.1               tzdb_0.4.0                DBI_1.1.3                 tweenr_2.0.2             
 [99] MASS_7.3-60               Matrix_1.5-4.1            cli_3.6.1                 parallel_4.2.2            igraph_1.5.0.1            pkgconfig_2.0.3           plotly_4.10.2            
[106] spatstat.sparse_3.0-2     bslib_0.5.0               XVector_0.38.0            digest_0.6.33             sctransform_0.3.5         RcppAnnoy_0.0.21          tsne_0.1-3.1             
[113] spatstat.data_3.0-1       Biostrings_2.66.0         rmarkdown_2.23            leiden_0.4.3              uwot_0.1.16               DelayedMatrixStats_1.20.0 shiny_1.7.4.1            
[120] lifecycle_1.0.3           nlme_3.1-162              jsonlite_1.8.7            viridisLite_0.4.2         fansi_1.0.4               pillar_1.9.0              ggsci_3.0.0              
[127] lattice_0.21-8            KEGGREST_1.38.0           fastmap_1.1.1             httr_1.4.6                survival_3.5-5            glue_1.6.2                png_0.1-8                
[134] bit_4.0.5                 tcltk2_1.2-11             class_7.3-22              stringi_1.7.12            sass_0.4.7                blob_1.2.4                memoise_2.0.1            
[141] irlba_2.3.5.1             e1071_1.7-13              future.apply_1.11.0       ape_5.7-1                
---
title: "4_A549_27plex_analysis"
output: html_notebook
---

Down stream analysis of the dataset '4_A549_27plex'

## setup

```{r setup}
knitr::opts_chunk$set(dev = "png",dpi = 80)
library(ggforce)
library(tidyverse)
```

```{r load tools}
dcolor <- ggsci::scale_color_d3('category20')
dfill <- ggsci::scale_fill_d3('category20')
ccolor <- viridis::scale_color_viridis()
style <- function(x,color,method='UMAP',axis=c(1,2),Theme=NULL,
                  axis.text=FALSE,axis.title=FALSE,coord_fix =TRUE,
                  palette=TRUE,title=NULL,
                  legend=TRUE,scale_color_log10=FALSE,
                  guide_pointSize=2.5,...) {
  axis <- paste0(method,axis)
  if(is.null(Theme)) Theme <- theme_bw
  g <- ggplot() + Theme()
  g <- g + geom_point(aes_(x=as.name(axis[1]),y=as.name(axis[2]),color=as.name(color)),x,...)
  
  if(coord_fix) {
    g <- g + coord_fixed()
  }
  if(!axis.text) {
    g <- g + theme(axis.text = element_blank(),axis.ticks = element_blank())
  }
  if(!axis.title) {
    g <- g + theme(axis.title = element_blank())
  }
  if(is.null(title)) {
    g <- g + ggtitle(method) 
  }else if(!is.na(title)) {
    g <- g + ggtitle(title) 
  }
  if(!legend) g <- g + theme(legend.position = 'none')
  
  if(scale_type(as.matrix(x[,color]))=='discrete' & !is.null(guide_pointSize)) {
    g <- g + guides(color = guide_legend(override.aes = list(size=guide_pointSize)))
  }
  
  if(palette) {
    if(scale_type(as.matrix(x[,color]))=='discrete') {
      g <- g + ggsci::scale_color_d3('category20')
    }else{
      if(scale_color_log10){
        g <- g + viridis::scale_color_viridis(trans='log10')
      }else{
        g <- g + viridis::scale_color_viridis()
      }
    }
  }
  return(g)
}
tib2df <- function(tib,RowNames=1) {
  rn <- pull(tib,all_of(RowNames))
  df <- tib %>%
    select(-RowNames) %>%
    as.data.frame()
  rownames(df) <- rn
  return(df)
}
```


##  load data

```{r}
data <- tibble(
  path = list.files("4_A549_27plex/Raw/",full.names = T),
  sampleName = c('0h-r1', '0h-r2', '0p5h-r1', '0p5h-r2', '24h-r1', '24h-r2', '48h-r1', '48h-r2')
  ) %>%
  separate(sampleName,into=c('treat','rep'),remove = F) %>%
  mutate(
    metric = map(path,~{
      .x %>%
        read_csv(show_col_types=F) %>% 
        unite(cell,tileID,cellID,sep = '-',remove = F)
      }),
    IF = map(metric,~{.x[,-(2:8)]}),
    meta = map2(metric,IF,~{
      .x[,1:8] %>%
        unite(cell,tileID,cellID,sep = '-',remove = F) %>%
        mutate(totalExp = rowSums(.y[,-1]))
      }),
    ) %>%
  select(-path,-metric)
```


## Quality check & control

```{r}
data %>%
  select(-IF) %>% unnest(meta) %>%
  ggplot(aes(x,y,color=treat)) +
    theme_void() +
    geom_point(size=.1) + dcolor +
    facet_grid(rep~treat) + coord_fixed()
```

```{r}
data %>%
  select(sampleName,meta) %>% unnest(meta) %>%
  ggplot(aes(sampleName,totalExp)) + geom_violin() + geom_boxplot()
```

```{r}
dataf <- data %>%
  mutate(
    idx = map(meta,~{.x$totalExp > 1.3}),
    IF = map2(IF,idx,~{.x[.y,]}),
    meta = map2(meta,idx,~{.x[.y,]})
    ) %>%
  mutate(
    idx2 = map(IF,~{rowSums(.x[,-1]==0) == 0}),
    IF = map2(IF,idx2,~{.x[.y,]}),
    meta = map2(meta,idx2,~{.x[.y,]}),
    nCell = map(idx2,sum) %>% unlist()
    ) %>%
  select(-idx,-idx2)

dataf %>%
  select(sampleName,meta) %>% unnest(meta) %>%
  ggplot(aes(sampleName,totalExp)) + 
  geom_violin() + geom_boxplot()
```

```{r}
dataf %>%  
  ggplot(aes(sampleName,nCell)) + 
  theme_classic() +
  geom_bar(stat='identity') +
  scale_x_discrete(limits=rev) +
  theme(axis.title.y = element_blank()) +  
  coord_flip() + theme(aspect.ratio = .35) 
```

```{r}
tmp <- dataf %>% select(sampleName,IF) %>% unnest(IF)
idx <- with(tmp,model.matrix(~0+sampleName))
colnames(idx) <- sub("sampleName","",colnames(idx))
avgmat <- tmp[,-(1:2)] %>% as.matrix() %>% t %>% {t(.%*%idx)/colSums(idx)} %>% scale %>% asinh %>% t 
pheatmap::pheatmap(avgmat,cluster_cols = F,cellwidth = 10,color=viridis::viridis(10),show_rownames = T)
```


```{r}
Meta <- dataf %>%
  select(sampleName,treat,rep,meta) %>% unnest(meta) %>%
  unite(Cell,treat,rep,cell,sep = '-',remove = F)
X <- bind_rows(dataf$IF) %>% select(-cell) %>% 
   scale() %>% asinh()
rownames(X) <- Meta$Cell
```

```{r}
p <- prcomp(X)
screeplot(p,type='l',n=30)
```

```{r}
total <- bind_rows(dataf$meta) %>% pull(totalExp) 
cor(p$x,total) %>%
  plot(xlab = 'PC',ylab = 'CorCoef_vsTotalExp')
```

```{r fig.asp=.5}
um <- uwot::umap(p$x[,2:6],
                 metric = 'cosine',
                 n_neighbors = 30,
                 min_dist = .1) %>%
  as_tibble(rownames = 'Cell') %>%
  dplyr::rename(UMAP1=2,UMAP2=3)
Metaum <- inner_join(Meta,um,by = 'Cell')

style(Metaum,'treat',title = NULL,size = .1,alpha = .6)
style(Metaum,'sampleName',title = NA,size = .1,alpha = .6)
style(Metaum,'totalExp',title = NA,size = .1,alpha = .6)
```



## Pseudo time

Using the datasets of 0h and 0.5h treatment

```{r}
Meta2 <- Meta %>% filter(treat %in% c('0h','0p5h'))
X2 <- dataf %>%
  filter(treat %in% c('0h','0p5h')) %>%
  pull(IF) %>% bind_rows() %>% 
  select(-cell) %>% 
  scale() %>% asinh()
rownames(X2) <- Meta2$Cell
```

```{r}
p2 <- prcomp(X2)
screeplot(p2,type='l',n=30)
```

```{r}
total2 <- Meta2$totalExp
cor(p2$x,total2) %>%
  plot(xlab = 'PC',ylab = 'CorCoef_vsTotalExp')
```

```{r}
um2 <- uwot::umap(p2$x[,2:6],
                 metric = 'cosine',
                 n_neighbors = 30,
                 min_dist = .1) %>%
  as_tibble(rownames = 'Cell') %>%
  dplyr::rename(UMAP1=2,UMAP2=3)
Metaum2 <- inner_join(Meta2,um2,by = 'Cell')
style(Metaum2,'treat',title = NULL,size = .1,alpha = .6)
```


```{r}
ph2 <- phateR::phate(p2$x[,2:6],
                     mds.dist.method = 'cosine',
                     knn = 20)
Metaum2 <- Metaum2 %>%
  mutate(PHATE1 = ph2$embedding[,1],PHATE2 = ph2$embedding[,2])
style(Metaum2,'treat','PHATE',size=.1)
style(Metaum2,'rep','PHATE',size=.1,palette = F)
```


```{r}
set.seed(1); km <- kmeans(ph2$embedding,centers=5)
Metaum2 %>% 
  mutate(km=as.factor(km$cluster)) %>%
  style('km','PHATE',size=.1)
```

```{r}
library(slingshot)
sce <- getLineages(ph2$embedding,as.character(km$cluster),start.clus = '5') %>%
  getCurves() %>% slingPseudotime() %>%
  as_tibble(rownames='Cell') %>% mutate(Lineage1 = Lineage1/max(Lineage1)) %>%
  rename(PseudoTime = Lineage1)
Metaum2 <- left_join(Metaum2,sce,by='Cell')
Metaum2 %>% style("PseudoTime","PHATE",size=.2) + theme_void()
Metaum2 %>% style("PseudoTime","UMAP",size=.2) + theme_void()
```

```{r}
Metaum2 %>%
  ggplot(aes(PseudoTime,fill=treat)) + geom_histogram(position = 'dodge') + theme_classic() + dfill + coord_fixed(ratio=.0003) +
  theme(axis.ticks = element_blank(),panel.grid = element_blank())
```

```{r}
LOESS <- dataf %>%
  filter(treat %in% c("0h","0p5h")) %>%
  select(sampleName,IF) %>% unnest(IF) %>%
  unite(Cell,sampleName,cell,sep='-') %>%
  left_join(select(Metaum2,Cell,PseudoTime),.) %>%
  gather(Prot,ExpLevel,-(1:2)) %>%
  group_by(Prot) %>% nest() %>%
  mutate(LOESS = map(data,~loess(ExpLevel~PseudoTime,.x,span = 0.2)),
         PRED = map(LOESS,~predict(.x,newdata=seq(0,1,by=0.01))),
         newdata = map(PRED,~tibble(PseudoTime=seq(0,1,by=0.01),PRED=.x)) )

loessPl <- function(x,ratio = .35){
  ggplot(x,aes(PseudoTime,scaled,color=Prot)) + 
  geom_line() + theme_bw() +
  theme(legend.position = 'none') +
  theme(axis.ticks.y = element_blank(),
        strip.background = element_rect(fill=NA,color=NA), 
        panel.grid.major.x = element_blank(),panel.grid.minor = element_blank()) + 
  coord_fixed(ratio=ratio) + labs(y = 'scaled expression level')
}

LOESS_scaled <- LOESS %>% select(Prot,newdata) %>% 
  mutate(newdata = map(newdata,~mutate(.x,scaled = (PRED-min(PRED))/(max(PRED)-min(PRED)) ))) %>%
  unnest(newdata)  %>% ungroup()

usep <- c("p4EBP1/2/3","pATM","pELF2A","pERK","pFAK","pp38","pSMAD1/5","pSMAD3","pSTAT3")
LOESS_scaled %>% filter(Prot %in% usep) %>% loessPl()
LOESS_scaled %>% filter(Prot %in% usep) %>% loessPl() + facet_wrap(~Prot)
```

```{r}
LOESS_scaled %>% filter(!Prot %in% usep) %>% loessPl() + facet_wrap(~Prot)
LOESS_scaled %>% filter(!Prot %in% usep) %>% loessPl()
```

```{r}
sessionInfo()
```

